Hyppää sisältöön
    • FI
    • ENG
  • FI
  • /
  • EN
OuluREPO – Oulun yliopiston julkaisuarkisto / University of Oulu repository
Näytä viite 
  •   OuluREPO etusivu
  • Oulun yliopisto
  • Avoin saatavuus
  • Näytä viite
  •   OuluREPO etusivu
  • Oulun yliopisto
  • Avoin saatavuus
  • Näytä viite
JavaScript is disabled for your browser. Some features of this site may not work without it.

Structured modeling of joint deep feature and prediction refinement for salient object detection

Xu, Yingyue; Xu, Dan; Hong, Xiaopeng; Ouyang, Wanli; Ji, Rongrong; Xu, Min; Zhao, Guoying (2020-02-27)

 
Avaa tiedosto
nbnfi-fe2020061042528.pdf (1.546Mt)
nbnfi-fe2020061042528_meta.xml (43.52Kt)
nbnfi-fe2020061042528_solr.xml (37.09Kt)
Lataukset: 

URL:
https://doi.org/10.1109/ICCV.2019.00389

Xu, Yingyue
Xu, Dan
Hong, Xiaopeng
Ouyang, Wanli
Ji, Rongrong
Xu, Min
Zhao, Guoying
IEEE Computer Society
27.02.2020

Y. Xu et al., "Structured Modeling of Joint Deep Feature and Prediction Refinement for Salient Object Detection," 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea (South), 2019, pp. 3788-3797, doi: 10.1109/ICCV.2019.00389

https://rightsstatements.org/vocab/InC/1.0/
© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
https://rightsstatements.org/vocab/InC/1.0/
doi:https://doi.org/10.1109/ICCV.2019.00389
Näytä kaikki kuvailutiedot
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2020061042528
Tiivistelmä

Abstract

Recent saliency models extensively explore to incorporate multi-scale contextual information from Convolutional Neural Networks (CNNs). Besides direct fusion strategies, many approaches introduce message-passing to enhance CNN features or predictions. However, the messages are mainly transmitted in two ways, by feature-to-feature passing, and by prediction-to-prediction passing. In this paper, we add message-passing between features and predictions and propose a deep unified CRF saliency model. We design a novel cascade CRFs architecture with CNN to jointly refine deep features and predictions at each scale and progressively compute a final refined saliency map. We formulate the CRF graphical model that involves message-passing of feature-feature, feature-prediction, and prediction-prediction, from the coarse scale to the finer scale, to update the features and the corresponding predictions. Also, we formulate the mean-field updates for joint end-to-end model training with CNN through back propagation. The proposed deep unified CRF saliency model is evaluated over six datasets and shows highly competitive performance among the state of the arts.

Kokoelmat
  • Avoin saatavuus [37559]
oulurepo@oulu.fiOulun yliopiston kirjastoOuluCRISLaturiMuuntaja
SaavutettavuusselosteTietosuojailmoitusYlläpidon kirjautuminen
 

Selaa kokoelmaa

NimekkeetTekijätJulkaisuajatAsiasanatUusimmatSivukartta

Omat tiedot

Kirjaudu sisäänRekisteröidy
oulurepo@oulu.fiOulun yliopiston kirjastoOuluCRISLaturiMuuntaja
SaavutettavuusselosteTietosuojailmoitusYlläpidon kirjautuminen